Simulation framework to measure the effect of applying smart house on the power consumption

Abstract

Distribution network operators are becoming increasingly interested in accurately anticipating load characteristics at the low voltage level. Energy disaggregation could be one of the potential approaches to exploit the massive amount of smart meter data to fulfill the task. Proper individual home appliance modelling is critical to the performance of NILM. In this paper, a hierarchical hidden Markov model (HHMM) framework to model home appliances is proposed. This model aims to provide better representation for those appliances that have multiple built-in modes with distinct power consumption profiles, such as washing machines and dishwashers. The dynamic Bayesian network representation of such an appliance model is built. A forward backward algorithm, which is based on the framework of expectation maximization (EM), is formalized for the HHMM fitting process. Tests on publicly available data show that the HHMM and proposed algorithm can effectively handle the modelling of appliances with multiple functional modes, as well as better representing a general type of appliances. A disaggregation test also demonstrates that the fitted HHMM can be easily applied to a general inference solver to outperform conventional hidden Markov model in the estimation of energy disaggregation.